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AI-Driven Climate Modeling: Validation and Uncertainty Mapping – Methodologies and Challenges
Get PDFClimate models are fundamental for predicting future climate conditions and guiding mitigation and adaptation strategies. This study aims to enhance the accuracy and reliability of climate modeling by integrating artificial intelligence (AI) techniques for validation and uncertainty mapping. AI-driven approaches, including machine learning-based parameterization, ensemble simulations, and probabilistic modeling, offer improvements in model precision, quality assurance, and uncertainty quantification. A systematic review methodology was applied, selecting peer-reviewed studies from 2000 to 2023 that focused on climate modeling, validation, and uncertainty estimation. Data sources included observational records, satellite measurements, and global reanalysis datasets. The study analyzed key AI-driven methodologies used for improving model accuracy, including statistical downscaling techniques and deep learning-based uncertainty prediction frameworks. Findings indicate that AI-enhanced models significantly improve climate projections by refining parameterization, enhancing bias correction, and optimizing uncertainty quantification. Machine learning applications facilitate more accurate predictions of meteorological phenomena, including temperature and precipitation variability. However, challenges remain in addressing observational biases, inter-model inconsistencies, and computational limitations. The study concludes that AI-driven advancements provide critical improvements in climate model reliability, yet ongoing refinements are necessary to address persistent uncertainties. Enhancing observational datasets, refining computational techniques, and strengthening model validation frameworks will be essential for reducing uncertainty. Effective communication of climate model outputs, including uncertainty mapping, is crucial for supporting informed policy decisions. AI-driven climate modeling is a rapidly evolving field, and continuous innovation will be key to improving predictive accuracy and resilience in climate adaptation strategies
Research on Intelligent Recommendation Algorithm of Short Videos Based on Graph Neural Network
Get PDFThe rapid development of short video platforms has put forward higher requirements for the accuracy and personalization of content recommendation systems. In this paper, a short video recommendation algorithm based on Graph Neural Network (GNN) is studied, which improves the recommendation performance by fusing multimodal features such as video, audio, and text. The key technologies such as graph convolution neural network, graph attention network and graph pooling operator are analyzed, and a multimodal recommendation framework is constructed by combining self-supervised contrastive learning and local feature encoder to effectively deal with complex user-content interactions. In this paper, several algorithms are compared on TikTok and MovieLens datasets. The experimental results show that the SHL algorithm significantly improves the recommendation accuracy and user personalized satisfaction on TikTok and MovieLens datasets, which is generalizable.
Enhancing Adaptive Learning Through Spectrum of Individuality Theory: A Neuroplasticity-Informed AI Approach to Dynamic Behavioral Modeling in Education
Get PDFThis study investigates the efficacy of integrating the Spectrum of Individuality Theory (SIT)—a dynamic, neuroplasticity-informed framework—into artificial intelligence (AI) systems for adaptive learning. Traditional AI models, rooted in static personality frameworks like the Five-Factor Model (FFM), often fail to capture real-time behavioral variability, limiting their adaptability. In a mixed-methods experiment, 120 undergraduate students were stratified into SIT-driven (n=60) and FFM-based (n=60) AI learning groups. The SIT system utilized real-time EEG and eye-tracking data to adjust content delivery, while the FFM system relied on fixed trait categorizations. Results demonstrated that the SIT group outperformed the FFM group in cognitive retention (mean post-test scores: 25.3 vs. 22.7; p < 0.01, Cohen’s d = 0.86) and exhibited progressive engagement improvements (Session 8 UES: 4.30 vs. 3.70; p < 0.001). Neurophysiological data revealed reduced stress biomarkers (theta/beta ratios: 3.15 vs. 3.75; p < 0.001), correlating with enhanced emotional regulation. However, ethical concerns, particularly data privacy (SIT: 4.10 vs. FFM: 3.20; d = 0.98), were heightened in the SIT group. These findings validate SIT’s potential to advance context-aware AI but underscore ethical risks tied to granular behavioral tracking. The study bridges psychological theory with AI design, advocating for interdisciplinary collaboration to balance adaptability with responsible innovation
Design of AI in leadership
Get PDFThe present research aims to demonstrate the dominance of AI-based technologies over the Leadership sector in Industry 4.0 by combining the two main industries, such as "artificial intelligence" and "leadership." Artificial Intelligence (AI) has had a notable impact on the technical and social working environment due to the growing use of AI-supported technology. In particular, to recognise and address the needs and difficulties faced by leaders in the majority of organisations. The current essay emphasises how crucial leadership is to the adoption and use of AI in business. It has been thought that a thorough examination of the literature studies now in existence would demonstrate the need for AI-supported leadership techniques in businesses. The research divided leadership into four categories: the Process of Strategic Transformation, Competencies and Qualification, Culture, and the Interaction of Human-AI. This division was made based on the analysis of the literature review. The study\u27s findings provide potential paths for further research and growth, as well as a thorough view
Advances in Vertical Farming: The Role of Artificial Intelligence and Automation in Sustainable Agriculture
Get PDFVertical farming has emerged as a sustainable agricultural method, resolving the issues of land scarcity, environmental consequences, and food security in urban and highly populated areas. The inclusion of artificial intelligence (AI) and automation into vertical farming systems improves their efficiency, production, and adaptability. The study highlights recent breakthroughs in AI-driven systems, spanning data analytics, predictive modeling, and autonomous control, which enhance critical parameters such as light, temperature, humidity, and nutrient delivery. Significant advancements in agricultural automation, including robotic technologies for planting, monitoring, and harvesting, are emphasized for their capacity to decrease labor expenses and enhance yield accuracy. Further, research evaluates the environmental effect, scalability, and practicality of automated vertical farming systems, examining the contribution of renewable energy and optimal use of resources to the development of resilient food production methods. This discussion addresses future directions and issues seeking to shed light on how AI and automation are shifting vertical farming into an important aspect of sustainable agriculture
Analysis of the use of artificial intelligence in the management of logistics processes: Approaches and benefits
Get PDFThis study explores artificial intelligence (AI) applications to improve logistics within the Belt and Road Initiative’s Middle Corridor, focusing on Ukraine amid geopolitical disruptions. Its objective is to quantify AI\u27s impact on delivery times, inventory costs, fuel consumption, and network resilience in this complex region. Employing a mixed-method approach, the research combines Social Network Analysis (SNA) to identify key nodes and bottlenecks with simulation modelling based on empirical data from 2019 to 2023, including GPS tracking and port metrics. Results show AI integration reduces delivery times by 11,7 %, cuts inventory holding costs by 16,3 %, and lowers fuel consumption by 9,2 %. SNA reveals enhanced connectivity and efficiency at critical hubs such as Kyiv and Odesa, strengthening Ukraine’s strategic logistics role. The study concludes that AI-driven optimizations significantly boost corridor efficiency, resilience, and sustainability. It recommends future work on real-time data integration and broader AI applications to support adaptive, greener supply chains across politically sensitive trade routes
AI Integration in Education: A Correlational Study on Attitudes, Perceptions and Anxiety Among Pre-Service Teachers
Get PDFThis study investigated pre-service teachers’ attitudes, perceptions, and anxiety toward artificial intelligence (AI), with gender, age, and socioeconomic status (SES) as demographic factors. Using a descriptive-quantitative correlational design, data were collected through an online survey from 150 valid respondents. Attitude was measured using the General Attitudes toward AI Scale, perception through an adapted 38-item instrument, and anxiety through the AI Anxiety Scale with four subscales. Descriptive statistics summarized levels, while t-tests, ANOVA, and Pearson correlation tested differences and relationships among variables. Results showed that respondents exhibited a generally positive attitude on favorable statements (M = 3,40), but expressed reservations on negative items (M = 2,49). Their overall perception was neutral (M = 2,68), while AI-related anxiety was moderate (M = 4,40), with higher levels in job replacement and sociotechnical blindness. Gender differences were not significant for attitude and perception, but female respondents reported significantly higher anxiety than males (p = ,010, large effect). No significant differences were observed across age groups, while SES revealed no variation in attitude and perception but showed significant differences in anxiety (p = ,026), with middle-class and poor respondents scoring higher than low-income groups. Correlation analysis indicated a moderate positive relationship between perception and anxiety (r = ,464, p < ,001), while attitude showed weak and nonsignificant links with both. Overall, the findings suggest cautious openness to AI among pre-service teachers, underscoring the need for teacher education programs to integrate AI-focused training and ethical discourse to reduce anxiety and enhance readiness for responsible AI integration
Uncovering Online Collaborative Learning in Teaching English for Specific Purposes
Get PDFThis study aimed to investigate how ESP teachers facilitate online collaboration in teaching English. Furthermore, this study sought to explore ESP teachers’ and students’ collaboration learning (OCL) in public administration (PA) courses. To achieve this, the researchers utilized semi-structured questionnaires and open-ended questions to gather data, which were then analyzed thematically. The findings revealed that ESP teachers employ online collaboration in PA courses in three distinct phases: pre-OCL, during OCL, and post-OCL. They perceived OCL through four themes: context, interaction, impact, and challenges. However, it is important to note that this study did not incorporate the use of a true experiment or direct observation in either the control or class intervention, which is a limitation that should be addressed in future studies. Future studies should explore the potential benefits of ESP-administration-based augmented reality. This study implies that OCL in ESP teaching necessitates innovation and creativity from ESP teachers, as it extends beyond simply emphasizing communicative competence
AI evolution and its role in transforming the automation of commercial activities
Get PDFThis article examined the impact of artificial intelligence (AI) on the automation of business processes, focusing on how intelligent systems enhanced management efficiency and operational optimization. Special attention was given to cognitive neuro-fuzzy models and their role in transforming business processes in the digital era. The study was timely, considering the exponential growth of data and the complexity of modern organizational structures, which demanded fast, accurate, and adaptive management solutions. AI technologies provided such capabilities, while companies that failed to adopt them risked losing competitive advantage amid ongoing digital transformation. The study aimed to develop and justify a conceptual approach to automating business processes through AI. To achieve this, two primary methods were applied: cognitive modeling using semantic M-networks to reflect human imaginative thinking in process structures, and reinforcement learning to optimize processes based on feedback mechanisms. The methodology combined theoretical literature analysis, mathematical modeling, and empirical examination of real business processes. The findings demonstrated that integrating AI significantly improved overall business process efficiency by reducing complexity, costs, and feedback loops, while enhancing control, regulation, and financial outcomes. The M-network model illustrated how AI adapted processes to dynamic environments and supported decision-making through visualized cognitive maps. Future research directions included advancing cognitive learning algorithms to handle larger datasets, designing adaptive AI interfaces tailored to individual user behavior, and exploring AI’s influence on cross-functional collaboration to foster comprehensive digital management ecosystems
The construction of the enemy: from telenovela to Tik Tok, Latin American melodramatic telepolitics
Get PDFWide and diverse criteria have been planned on melodrama, from its overvaluation as a cultural reference, to its marginalization in contemporary academic debates. However, it constitutes one of the most representative discourses in Latin America, on which various types of cultural industries and forms of social coexistence converge. This paper proposes a position that identifies melodrama not only as a cultural scenario, but also as a political one. The objective is to reflect on how these discourses have been narratives appropriated by politicians to seduce the population with promises of social change and how they have shifted from traditional media to social networks. The question that articulates this essay is: In what way has melodrama become the prevailing political discourse in the region, and which have assumed the stories of soap operas as a platform to conquer the votes of the population, and which have been massified thanks to social networks?